State of the Art in Mobile Robot Localization
A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Sensors and Control in Robotics".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 97
Special Issue Editors
Interests: multisensory fusion techniques and perception systems applied to autonomous navigation in indoor and outdoor environments; precision agriculture; marine robotics; social robots; real-time system development using C++ and ROS
Interests: robot navigation; ROS; open-source robotics and planning
Interests: application of advanced robotic technologies with a focus on software architectures for behavior control in order to improve the care and treatment of older adults in real-world clinical and social environments
Special Issue Information
Dear Colleagues,
Accurate and robust localization is a fundamental prerequisite for autonomous mobile robots, directly influencing navigation, decision-making, and task execution. Over the past two decades, research has advanced from classical probabilistic frameworks toward sophisticated approaches that integrate sensor fusion, optimization, and machine learning. Nevertheless, persistent challenges remain, including operation under uncertainty, adaptability to unstructured and dynamic environments, scalability in large-scale deployments, and computational efficiency for real-time applications.
Recent developments in multi-modal sensing, graph-based optimization, deep learning, and cooperative multi-robot systems have reinvigorated localization research, providing novel methodologies that push the boundaries of robustness and generalization. This Special Issue aims to consolidate state-of-the-art progress, highlight emerging paradigms, and identify open problems that will shape the next generation of autonomous systems. We invite original research articles, surveys, and comparative studies that advance the theoretical foundations, algorithmic innovations, and experimental validations of mobile robot localization.
We look forward to receiving your contributions.
Prof. Dr. José Miguel Guerrero Hernández
Dr. Francisco Martin-Rico
Dr. Pérez-Rodríguez Rodrigo
Guest Editors
Manuscript Submission Information
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Keywords
- mobile robot localization and navigation
- simultaneous localization and mapping (SLAM)
- multi-sensor fusion (LiDAR, vision, GNSS, IMU, and UWB)
- probabilistic state estimation and Bayesian filtering
- deep learning, AI-driven, and hybrid localization methods
- robust localization in dynamic and uncertain environments
- cooperative and multi-robot localization strategies
- real-time localization and control for autonomous systems
- benchmarking, datasets, and reproducible research in localization
- applications in autonomous ground vehicles, UAVs, and service robots
- semantic localization and navigation
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